Using Biophysical Computational Neural Models to Investigate
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Using Biophysical Computational Neural Models to Investigate Neuropsychiatric Disorders Samuel Neymotin, PhD Nathan Kline Institute for Psychiatric Research Orangeburg, NY Computational Psychiatry, 02/20/2020 From computational neuroscience → computational psychiatry . Psychiatry has fundamental problems in defining and diagnosing mental disorders/illness, partly due to obvious difficulties in measuring origins of symptoms (brain is largely inaccessible). In order to better understand, diagnose, and treat mental disorders, clinicians/scientists should leverage insights gained from the integrative methodologies used in computational and experimental neuroscience. However, computational neuroscience is a complex field, taking years of training. To allow computational neuroscience and psychiatry to make the largest impact, new computational/algorithmic tools are needed that integrate multiscale brain dynamics and behavior, and allow scientists to rapidly test their hypotheses on the neural origins of mental disorders. (Images from Wikipedia) Outline: modeling tools (solutions) & modeling case studies 1. Build open-source modeling tools (Human Neocortical Neurosolver: HNN) to help researchers understand circuit level origins of human brain dynamics (EEG/MEG) 2. Use auditory thalamocortical models to link functionally relevant brain rhythms in non-human primate to human electrophysiological data 3. Use hippocampal network models to study schizophrenia 4. Use multi-scale models of primary motor cortex to study neocortical hyperexcitability and its pharmacological treatments 5. Use models of sensory/motor cortex to understand learning/behavior → beyond deep learning? 1. Problem in human neuroscience ●Question: How do we link human macroscopic, noninvasively measured MEG/EEG signals to their underlying cell/circuit-level generators? ●Data: Source localized human MEG/EEG current dipole signals ●Model: Biophysical circuit models of the thalamocortical system that generate current dipole signals directly comparable to experimental data ●Result: New open-source modeling tool allowing clinicians/researchers to import their data and use the model to simulate commonly observed patterns (event-related potentials, low frequency rhythms), enabling hypothesis testing of circuit-level generators of the observed patterns Neymotin et al., eLife 2020 New open-source modeling tool: Human Neocortical Neurosolver (HNN) ●Use biophysical thalamocortical models to test hypotheses on cell/circuit-level origins of human neural dynamics in health & disease ●Imports Human MEG/EEG data for model comparison/fitting ●https://hnn.brown.edu includes background, tutorials, documentation, publications (eLife) ●HNN workshops/presentations at Computational Psychiatry (http://computationalpsychiatry.org/cp18/), Cutting EEG, and SFN meetings, ... The HNN model simulates the primary currents contributing to MEG/EEG Cortical Column HNN’s laminar neocortical microcircuit model Layered cortical network of pyramidal neurons and interneurons Individual neurons are compartmental models using parallel conductance equations with standard Hodgkin-Huxley ion channels Pyramidal neurons generate current dipole signal directly comparable to source-localized signals obtained from MEG/EEG experiments Activating HNN’s microcircuit model Proximal drive represents synaptic inputs from thalamic core Distal drive represents synaptic inputs from thalamic matrix and corticocortical feedback Each type of drive pushes pyramidal neuron dendrite current flow in opposing directions Local cell/circuit interactions also influence current flow within pyramidal neuron dendrites HNN’s graphical user interface Low-frequency alpha/beta rhythms widely observed in human MEG/EEG signals, altered in disease Source-localized MEG signals from somatosensory cortex have transient alpha/beta events inversely correlated with attention/detection of tactile stimuli; alpha/beta events detectable in auditory/visual cortex, with similar function (i.e. inhibition of unattended modality: Lakatos et al., Nature Neurosci 2016 ) Low frequency oscillations (delta, theta, alpha, low gamma) are altered in schizophrenia (Lisman JAMA Psychiatry 2016; Lakatos et al., J Neurosci 2013; Kopell et al.) Using HNN to model alpha/beta rhythms Stochastic 10 Hz rhythmic inputs to proximal/distal dendrites in phase (antiphase) produce beta (alpha) rhythms/events Model beta mechanism validated with invasive laminar electrode array recordings (Sherman et al., PNAS 2016) Use the software to investigate origins of rhythms, how circuit alterations lead to reduced/enhanced ability to respond to stimuli in health and in disease Next: use invasive laminar electrode array (LFP/CSD/MUA) data from nonhuman primates to optimize/validate models and investigate auditory/speech processing 2. Integrating electrophysiology in nonhuman primates (NHP) during auditory stimulus/speech processing with computer modeling Question: How does thalamocortical circuitry generate and potentially use oscillations to support auditory & speech processing? Why does the circuitry fail to properly entrain to stimuli in neuropsychiatric disorders? Data: Multi-area/multilaminar ephys data (thalamus, A1, V1) at multiple scales (single-unit, multi-unit, LFP, CSD, ECoG) from Lakatos lab @ NKI; human iEEG data from S. Bickel (Northwell) Model: Detailed biophysical thalamocortical system circuits Result: Model generates LFP/CSD comparable to experiment, allowing prediction on circuit generators, mechanisms, and neuromodulation targets At rest: complex temporal pattern of oscillations Using the data (Lakatos lab) to optimize the model Integrating NHP electrophysiology during auditory processing with modeling 1. Determine mechanisms supporting flexible oscillations needed to track rhythmic auditory stimuli (speech). Model determines strengths of connectivity between cortex and thalamus, suggests ways to increase oscillation flexibility. 2. Determine thalamocortical mechanisms of oscillatory phase reset for aligning brain rhythms to stimuli, could be used to parse auditory objects. Model predicts in vivo neuromodulation strategies to improve this process. 3. Determine mechanisms supporting auditory object Different A1 L2/3 ensembles show phase formation, hypothesized to occur through periodic synchronization for vowels (< 8 KHz; inhibition. Model tests how different interneuron low-frequency tuned) or consonants (> 10 KHz; high-frequency tuned), which tend to populations contribute to this process, providing occur out-of-phase additional in vivo neuromodulation strategies. Model: neuronal populations Neurons: multi-compartment, conductance-based. Excitatory neurons: intratelencephalic (IT), pyramidal tract (PT), spiny stellate (ITS), corticothalamic (CT) and MGB thalamocortical (TC). Inhibitory neurons: somatostatin (SOM), parvalbumin (PV), neurogliaform (NGF), vasoactive intestinal peptide (VIP), and thalamic reticular nucleus (RT). Geometry: Simplified morphologies. Dendritic lengths sized to match the macaque cortex dimensions. Model built with NetPyNE platform (https://netpyne.org Dura-Bernal et al., eLife 2019) Model Development Challenge: pyramidal neurons have unknown spatial distribution of ion channels ●Full spatial channel distribution is unknown, but experimental literature indicates certain spatial constraints (e.g. HCN density increases distally) ●Requirement: develop detailed models with full dendrite reconstruction (~700 compartments) and simplified models (6 representative compartments) in order to produce accurate circuit dynamics ●Goal: optimize model channel densities (Na, K, Ca, HCN) in order to reproduce observed in vitro activity from current clamp recordings ●Solution: Use sequential optimization: 1. subthreshold fits; 2. firing property fits Neymotin et al., J Neurophysiology 2016 Model neuron optimization Cell types in each layer fitted to macaque or rodent electrophysiology data via multi-objective evolutionary optimization algorithm or via hand-tuning. Passive parameters (e.g. leak channel, HCN channel conductance, capacitance) were tuned to fit RMP and other features of subthreshold traces, including steady state voltage and sag. Active parameters (e.g. fast Na, K, Ca, BK channel density) were then tuned to fit features like firing rate curves, action potential shape, and adaptation. Neymotin et al., J Neurophysiology 2016 Model: data-driven circuit connectivity Experimental circuit mapping data constrains model connectivity E → E/I from mouse V1,S1,A1,M1 (Levy & Reyes 2012; Yoshi et al 2015; Billeh et al 2019; Lefort et al 2009; Allen Brain Institute) I → E/I from mouse V1,S1,A1,M1 (Sohn et al 2016; Naka et al 2016; Tremblay et al 2016; Pi et al 2013; Pi et al 2013) Model includes biophysically-detailed LFP → comparison with in vivo data The A1 model simulates laminar local field potentials, and will be used to determine the origin of different oscillation patterns observed in the data Providing auditory stimuli to the model Pathway: sound wave → cochlea → inferior colliculus (IC) → MGB → A1 Full pathway allows comparison of NHP and model data to determine circuit mechanisms supporting sound/speech processing ← UR Ear (Laurel Carney Lab) Biophysical thalamocortical system model to predict origin of different oscillations Model: thalamic core/matrix interconnected bidirectionally with neocortex → creates oscillations Neocortical neurons (pyramidal neurons, stellate cells, PV/SOM/VIP/NGF interneurons) arranged in 6 cortical layers; thalamic neurons